title: Using machine learning for improving knowledge on antibacterial effect of bioactive glass.
abstract: The aim of this work was to find relationships between critical bioactive glass characteristics and their antibacterial behaviour using an artificial intelligence tool. A large dataset including ingredients and process variables of the bioactive glasses production, bacterial characteristics and microbiological experimental conditions was generated from literature and analyzed by neurofuzzy logic technology. Our findings allow an explanation on the variability in antibacterial behaviour found by different authors and to obtain general conclusions about critical parameters of bioactive glasses to be considered in order to achieve activity against some of the most common skin and implant surgery pathogens.
authors.affiliation: Departamento Farmacia y Tecnología Farmacéutica, Facultad de Farmacia, Campus Vida, Universidad de Santiago, Santiago de Compostela 15782, Spain. mmagdalena.echezarreta@usc.es
authors.forename: M M
authors.initials: MM
authors.lastname: Echezarreta-López
authors.forename: M
authors.initials: M
authors.lastname: Landin
mesh_terms: D000900:Anti-Bacterial Agents; D001185:Artificial Intelligence; D001419:Bacteria / Q000254:growth & development*; D005898:Glass; D008962:Models, Theoretical
chemical_list: D000900:Anti-Bacterial Agents
keywords: Antibacterial behaviour; Artificial intelligence; Bioactive glass; Machine learning; Modelling; Neurofuzzy logic